Knowledge on the most exposed areas of a city constitutes an important basis for suitable short and long-term planning. We present and compare three different methods that allow us to assess the potential heat exposure for the smallest administrative spatial units, the quarters, in the German city of Karlsruhe which was repeatedly affected by heat waves in the past. The three methods are based on (1) meteorological station data from the city and its hinterland, (2) a city climate index and (3) remote sensing data. The aim is to answer the question whether different approaches provide different levels of heat exposure. By comparing the three methods we could identify regions by cross validation where the level of heat exposure is highly confident. Regions where one model result deviates from that of another, give interesting insights in the interrelation of features of the method and circumstances in the study area. Regions were all the three models showed different results remained very rare. The results may be relevant for decision-makers who want to implement small-scale measures for heat mitigation but only have limited resources available.

As many cities are increasingly affected by heat waves, knowledge regarding those parts of cities most susceptible to heat exposure is essential for the implementation of directed adaptation measures. The frequency of heat waves is projected to increase in both German cities considered for this study: Karlsruhe and Berlin. By aggregating temperature data from meteorological stations within the two cities and their hinterlands, the local temperature distribution within the administrative city boundaries was assessed. A multiple regression approach was used to reveal the regional inter-relationship between non-meteorological factors such as altitude, population density and land use, on the one hand, and the heat distribution, on the other. This functional relationship was then applied at the city quarter level for the two cities. A model selection process was undertaken to find the most significant models describing the heat exposure of two heat indicators: heat wave days (HWDs) and tropical nights (TRNs). While altitude and population density were found to be the most significant explanatory variables for Karlsruhe, population density had a dominating influence on the distribution of heat at the city quarter level for Berlin. In Karlsruhe, models describing the daytime temperature performed best, whereas in Berlin those describing the night time temperature distribution had the highest statistical significance. This method could be used with relatively low financial and material expense to assess heat exposure in different city quarters even if there are insufficient meteorological stations within a city.

In order to achieve meaningful climate protection targets at the global scale, each country is called to set national energy policies aimed at reducing energy consumption and carbon emissions. By calculating the monthly heating energy demand of dwellings in the Netherlands, our case study country, we contrast the results with the corresponding aspired national targets. Considering different future population scenarios, renovation measures and temperature variations, we show that a near zero energy demand in 2050 could only be reached with very ambitious renovation measures. While the goal of reducing the energy demand of the building sector by 50% until 2030 compared to 1990 seems feasible for most provinces and months in the minimum scenario, it is impossible in our scenario with more pessimistic yet still realistic assumptions regarding future developments. Compared to the current value, the annual renovation rate per province would need to be at least doubled in order to reach the 2030 target independent of reasonable climatic and population changes in the future. Our findings also underline the importance of policy measures as the annual renovation rate is a key influencing factor regarding the reduction of the heating energy demand in dwellings.

The electricity system is particularly susceptible to climate change due to the close interconnectedness between electricity production, consumption and climate. This study provides a country based relative analysis of 21 European countries' electricity system susceptibility to climate change. Taking into account 14 quantitative influencing factors, the susceptibility of each country is examined both for the current and projected system with the result being a relative ranked index. Luxembourg and Greece are the most susceptible relatively due in part to their inability to meet their own electricity consumption demand with inland production, and the fact that the majority of their production is from more susceptible sources, primarily combustible fuels. Greece experiences relatively warm mean temperatures, which are expected to increase in the future leading to greater summer electricity consumption, increasing susceptibility. Norway was found to be the least susceptible, relatively, due to its consistent production surplus, which is primarily from hydro (a less susceptible source) and a likely decrease of winter electricity consumption as temperatures rise due to climate change. The findings of this study enable countries to identify the main factors that increase their electricity system susceptibility and proceed with adaptation measures that are the most effective in decreasing susceptibility.

The housing sector is a major consumer of energy. Studies on the future energy demand under climate change which also take into account future changes of the building stock, renovation measures and heating systems are still lacking. We provide the first analysis of the combined effect of these four influencing factors on the future energy demand for room conditioning of residential buildings and resulting greenhouse gas (GHG) emissions in Germany until 2060. We show that the heating energy demand will decrease substantially in the future. This shift will mainly depend on the number of renovated buildings and climate change scenarios and only slightly on demographic changes. The future cooling energy demand will remain low in the future unless the amount of air conditioners strongly increases. As a strong change in the German energy mix is not expected, the future GHG emissions caused by heating will mainly depend on the energy demand for future heating.